Zingle is a platform that automates the documentation of data assets by analyzing codebases, data pipelines, and business documents to ensure metadata is complete and accurate. This addresses the issue of incomplete documentation, which often leads to inaccuracies in AI applications, saving data teams over 15 hours per week.
Funding
Funding not disclosed
Founders
Product
Problem
Data teams often struggle with incomplete or outdated documentation of data assets, leading to inaccuracies in AI applications and hindering data discovery. Manually maintaining metadata across codebases, data pipelines, and business documents is time-consuming and prone to errors.
Solution
Zingle provides an automated data documentation platform that analyzes codebases, data pipelines, SQL queries, and business documents to ensure metadata is complete, accurate, and up-to-date. The platform identifies gaps in existing documentation and suggests additions, updates, or corrections. By providing complete context for data assets like tables, columns, and dashboards, Zingle enables data teams to build more accurate AI applications and improve data governance. The platform also features a robust search functionality, allowing data analysts, data engineers, and software developers to easily find and access the data documentation they need, reducing the need for constant communication and improving overall efficiency.
Target Audience
Zingle is designed for data analysts, data engineers, data scientists, and other data professionals who need to maintain accurate and complete data documentation for AI development, data governance, and data discovery.
Features
- Automated metadata extraction from codebases, data pipelines, and business documents
- Identification of missing or outdated documentation elements
- Suggestion of additions, updates, and corrections to existing documentation
- Support for 30+ data sources, including PostgreSQL, MySQL, BigQuery, MS SQL, and Snowflake
- Docker image for self-hosting, ensuring data privacy and security
- Role-based approvals for documentation verification
- API access for integrating metadata into RAG applications
- SQL Playground for testing and validating documentation